Hudson Data

Programmer : Python + SQL Developer (Backend + Data Analytics) Role: We are looking for a versatile Python + SQL Developer who can work across both backend application development and data analytics workflows . You will build scalable APIs, automate data pipelines, and enable data-driven decision-making through analytical scripting and reporting. Key Responsibilities Develop and maintain backend systems and REST APIs using Python (Flask, FastAPI, or Django). Design and optimize relational databases (e.g., PostgreSQL, MySQL) for both application logic and analytics. Build and automate ETL workflows for internal data processing and reporting. Write complex SQL queries for data extraction, transformation, and aggregation. Generate analytical outputs, dashboards, and insights using Python (Pandas, Matplotlib/Plotly). Collaborate with cross-functional teams (Product, Ops, Data) to deliver data-backed features and reports. Expert in Pivot Tables in Excel Desired Candidate Profile Required Skills Proficiency in Python for backend and scripting tasks. Strong expertise in SQL and relational databases . Experience building APIs and backend services using Flask , FastAPI , or Django . Hands-on with Pandas , NumPy , and other analytics libraries. Hands on Pivot Table Ability to write reusable, well-structured code with good documentation. Experience with version control tools like Git . Job Benefits & Perks As per General IT industry Practices

Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 3+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices

Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 3+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices

Role & responsibilities We are seeking a skilled Cybersecurity Engineer / Specialist with strong expertise in Google Cloud Platform (GCP) security and cloud management . The role involves securing cloud infrastructure, monitoring threats, ensuring compliance, and building automation to protect mission-critical systems. You will collaborate with DevOps and engineering teams to design and implement best-in-class cloud security practices. Responsibilities: Design, implement, and manage secure GCP workloads including IAM, VPCs, firewall policies, and encryption. Deploy and maintain Google Security Command Center, Chronicle SIEM, Cloud Armor, and KMS . Monitor and investigate security incidents, vulnerabilities, and anomalies in cloud environments. Conduct risk assessments, compliance checks, and audits (ISO 27001, SOC2, GDPR, HIPAA). Automate cloud security processes using Terraform, Python, and CI/CD integrations . Partner with DevOps and Infrastructure teams to embed DevSecOps best practices . Preferred candidate profile 3 / 7 years of experience in cybersecurity with at least 2 years on GCP security . Strong knowledge of IAM, SCC, Chronicle, DLP, Cloud Armor, KMS, and VPC security . Hands-on with security automation and scripting (Python, Bash, Terraform). Experience in incident response, vulnerability management, and SIEM/SOAR tools . Familiar with regulatory frameworks and compliance requirements. Certifications preferred: Google Professional Cloud Security Engineer, CISSP, CEH, or CISM .

Role & responsibilities We are seeking a skilled Google cloud Developer / Specialist with strong expertise in Google Cloud Platform (GCP) security and cloud management . The role involves securing cloud infrastructure, monitoring threats, ensuring compliance, and building automation to protect mission-critical systems. You will collaborate with DevOps and engineering teams to design and implement best-in-class cloud security practices. Responsibilities: Design, implement, and manage secure GCP workloads including IAM, VPCs, firewall policies, and encryption. Deploy and maintain Google Security Command Center, Chronicle SIEM, Cloud Armor, and KMS . Monitor and investigate security incidents, vulnerabilities, and anomalies in cloud environments. Conduct risk assessments, compliance checks, and audits (ISO 27001, SOC2, GDPR, HIPAA). Automate cloud security processes using Terraform, Python, and CI/CD integrations . Partner with DevOps and Infrastructure teams to embed DevSecOps best practices . Preferred candidate profile 3 / 7 years of experience in cybersecurity with at least 2 years on GCP security . Strong knowledge of IAM, SCC, Chronicle, DLP, Cloud Armor, KMS, and VPC security . Hands-on with security automation and scripting (Python, Bash, Terraform). Experience in incident response, vulnerability management, and SIEM/SOAR tools . Familiar with regulatory frameworks and compliance requirements. Certifications preferred: Google Professional Cloud Security Engineer, CISSP, CEH, or CISM .

Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. Role: Data Collection : Gather data from various sources, including databases, spreadsheets, and external datasets. Data Cleaning : Ensure data quality by identifying and correcting errors or inconsistencies in the data. Data Analysis : Analyze data using statistical methods to uncover trends, patterns, and insights. Reporting : Create reports and visualizations to present findings to stakeholders in a clear and actionable manner. Tool Development : Develop and maintain dashboards or tools that facilitate ongoing data analysis and reporting.. Responsibilities: 1.Data Management : Maintain databases and ensure data integrity. Develop data models to support analysis. 2.Statistical Analysis : Use statistical techniques to analyze data sets. 3.Visualization : Create visualizations (charts, graphs, etc.) to communicate findings effectively. Use tools like Looker for reporting. 4.Business Insights : Interpret data to provide actionable insights to improve business performance. Identify trends and make recommendations based on analysis. 5 Documentation : Document processes, methodologies, and findings to ensure transparency and reproducibility. Prepare user guides or training materials for tools developed. 6.Continuous Improvement : Stay updated on industry trends and best practices in data analysis. Seek opportunities to improve processes and tools for more effective data analysis. Desired Candidate Profile 6+ years of work experience in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data Analytics responsibilities and tasks. Experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative. A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience is required. Experience in Fintech and/or Insurance industry will be a preferred Job Benefits & Perks As per General IT industry Practices

Why Hudson Data Work on real data problems across fintech, lending, and AI-ops platforms. Exposure to full-stack analytics: modeling, automation, and product integration. Flat structure, high ownership, and visible impact on business outcomes. Continuous learning and mentorship from experienced data and product leaders. Role & responsibilities Hudson Data is a fast-growing analytics and AI company helping financial and technology clients turn raw operational data into predictive insights. We combine domain expertise, modern data infrastructure, and applied machine learning to solve high-value business problems in credit, collections, risk, and customer engagement. Were looking for a Data Scientist who thrives on execution someone who can own end-to-end analytics assignments, from data extraction to deployment, and deliver measurable impact to business outcomes. Responsibilities: As a Data Scientist at Hudson Data, you will build, validate, and deploy data-driven models and analytical solutions that directly improve operational KPIs such as repayment rates, lead conversion, risk segmentation, and campaign efficiency. Youll work closely with cross-functional teams in product, engineering, and operations to turn analytical insights into scalable, production-ready tools.Design and execute end-to-end data science projects from problem framing to deployment. Collect, clean, and integrate data from multiple systems (SQL, Excel, APIs, Snowflake, AWS). Build and optimize predictive models Validate and monitor model performance on live data; retrain as needed. Automate recurring analytical workflows using Python, SQL, and other data pipelines. Present actionable insights through dashboards and concise summaries for business teams. Partner with engineering to productionize models via APIs or embedded scoring scripts. Document datasets, code, and methodologies for transparency and reproducibility. Preferred candidate profile 5 to 7 years of hands-on experience in applied data science or analytics. Strong programming skills in Python (pandas, scikit-learn, NumPy) and SQL. Proven experience with predictive modeling, regression, classification, or clustering. Familiarity with cloud platforms (AWS / GCP / Azure) and version control (Git). Strong understanding of feature engineering, data validation, and model evaluation. Excellent communication skills and ability to translate analytical output into business impact.

Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation. As a Data Analyst and Modelling professional, you will be responsible for collecting, processing, and performing statistical analysis on large datasets to extract meaningful insights and build predictive frameworks that inform strategic business decisions. Key Responsibilities Data Collection and Management: Gather data from primary and secondary sources (databases, APIs, surveys, spreadsheets), and design and maintain data systems and databases to ensure data accuracy, integrity, and accessibility. Data Cleaning and Preprocessing: Filter, clean, and reorganize raw, complex datasets to fix coding errors, handle missing values, and remove duplicates and outliers, ensuring data quality and consistency for analysis. Data Analysis and Interpretation: Use statistical techniques (like regression analysis and hypothesis testing) to interpret data, analyze results, identify trends, patterns, and correlations, and diagnose why certain outcomes occurred. Data Modelling: Create conceptual, logical, and physical data models to define how data elements are organized, stored, and interact, supporting effective analysis and predictive capabilities. This includes developing predictive models using statistical techniques and machine learning algorithms to forecast future outcomes. Reporting and Visualisation: Create and maintain reports, interactive dashboards, graphs, and other visualisations to effectively communicate complex findings and recommendations to non-technical stakeholders and leadership. Collaboration and Communication: Work closely with cross-functional teams (including programmers, engineers, and managers) to understand business needs, define KPIs, and translate technical findings into actionable business strategies and solutions. Process Improvement: Evaluate internal systems for efficiency, identify opportunities for process improvements, and recommend system modifications and data governance policies. Required Skills & Qualifications Technical Skills: Programming Languages: Proficiency in SQL (Structured Query Language) for database querying and management; Python for statistical analysis, automation, and machine learning. Data Visualization Tools: Experience with Excel for creating reports and dashboards. Statistical Software: Knowledge of statistical packages like Excel, SPSS, or SAS for advanced analysis. Database Management: Understanding of database design, development, and ETL (Extract, Transform, Load) frameworks. Machine Learning : Familiarity with building basic predictive models and algorithms. Soft Skills: Strong analytical and problem-solving skills, with attention to detail. Excellent written and verbal communication skills, including data storytelling and presentation abilities. Critical thinking and a natural curiosity to explore data and uncover insights. Ability to work independently and collaboratively in a team-oriented, fast-paced environment. Strong organizational and project management skills. Qualifications: A bachelor's degree in a relevant field such as Statistics, Mathematics, Computer Science, Economics, or Information Management is typically required. A master's degree in Data Science or Business Analytics can be beneficial for senior-level roles. Relevant professional certifications (e.g., Google Data Analytics Professional Certificate, Microsoft Certified: Power BI Data Analyst Associate) and a portfolio of real-world projects are highly valued. Job Benefits & Perks: As per General IT industry Practices

About the Role We are looking for an experienced Full Stack Engineer with strong backend development experience and working knowledge of modern frontend frameworks like React & Angular . In this role, you will help design, build, and deploy web applications, contributing across both backend services and UI development when needed. You will work closely with the team to build cloud-native solutions using Python , Docker , Kubernetes , and Google Cloud Platform (GCP) . Key Responsibilities: Develop and maintain backend applications and APIs using Python (FastAPI, Django, or Flask). Write clean, maintainable, and well-tested backend code following best practices. Build and enhance frontend features using React or Angular where needed. Create containerized applications using Docker , ensuring efficient builds and deployments. Deploy, manage, and troubleshoot applications on Kubernetes (or GKE). Work with GCP services such as Compute Engine, Cloud SQL, Cloud Storage, and GKE to build reliable and scalable systems. Debug and resolve issues across the application stack (backend, frontend, infrastructure). Contribute to CI/CD pipelines, testing, environment setup, and general DevOps workflows. Maintain clear documentation for features, processes, and technical decisions. Technical Requirements Strong proficiency in Python , with experience using backend frameworks such as FastAPI, Django, or Flask . Experience building frontend interfaces using React or Angular . Hands-on experience working with Docker and writing efficient Dockerfiles. Practical experience deploying and managing applications on Kubernetes (or GKE). Working knowledge of Google Cloud Platform (GCP) services such as Compute Engine, Cloud SQL, Cloud Storage, or GKE. Solid understanding of databases (SQL & NoSQL) and designing efficient data models. Familiarity with Git-based workflows, code reviews, and modern development practices. Experience troubleshooting issues across the stack (backend, frontend, cloud, containers). Nice-to-Haves Experience with CI/CD tools (GitHub Actions, or Google Cloud Build). Familiarity with Infrastructure as Code tools such as Terraform or Ansible . Experience migrating frontend applications (e.g., Angular React). Understanding of monitoring and logging tools (Prometheus, Grafana, ELK, etc.). Knowledge of security best practices for cloud and application development. Soft Skills Excellent communication skills and ability to work cross-functionally. Strong ownership mindset with a focus on delivering high-quality solutions. Ability to thrive in a fast-paced environment and learn new technologies quickly. Proactive approach to problem-solving and continuous improvement. Education & Certifications Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. Google Cloud Professional Data Engineer AWS Certified Data Analytics Why Join Hudson Data At Hudson Data, youll be part of a dynamic, innovative, and globally connected team that uses cutting-edge tools from AI and ML frameworks to cloud-based analytics platforms to solve meaningful problems. You’ll have the opportunity to grow, experiment, and make a tangible impact in a culture that values creativity, precision, and collaboration.

About the Role We are seeking a Data Analyst & Modeling Specialist with a passion for leveraging AI, machine learning, and cloud analytics to improve business processes, enhance decision-making, and drive innovation. Youll play a key role in transforming raw data into insights, building predictive models, and delivering data-driven strategies that have real business impact. Key Responsibilities: 1. Data Collection & Management Gather and integrate data from multiple sources including databases, APIs, spreadsheets, and cloud warehouses. Design and maintain ETL pipelines ensuring data accuracy, scalability, and availability. Utilize any major cloud platform (Google Cloud, AWS, or Azure) for data storage, processing, and analytics workflows. Collaborate with engineering teams to define data governance, lineage, and security standards. 2. Data Cleaning & Preprocessing Clean, transform, and organize large datasets using Python (pandas, NumPy) and SQL. Handle missing data, duplicates, and outliers while ensuring consistency and quality. Automate data preparation using Linux scripting, Airflow, or cloud-native schedulers. 3. Data Analysis & Insights Perform exploratory data analysis (EDA) to identify key trends, correlations, and drivers. Apply statistical techniques such as regression, time-series analysis, and hypothesis testing. Use Excel (including pivot tables) and BI tools (Tableau, Power BI, Looker, or Google Data Studio) to develop insightful reports and dashboards. Present findings and recommendations to cross-functional stakeholders in a clear and actionable manner 4. Predictive Modeling & Machine Learning Build and optimize predictive and classification models using scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, and H2O.ai. Perform feature engineering, model tuning, and cross-validation for performance optimization. Deploy and manage ML models using Vertex AI (GCP), AWS SageMaker, or Azure ML Studio. Continuously monitor, evaluate, and retrain models to ensure business relevance. 5. Reporting & Visualization Develop interactive dashboards and automated reports for performance tracking. Use pivot tables, KPIs, and data visualizations to simplify complex analytical findings. Communicate insights effectively through clear data storytelling. 6. Collaboration & Communication Partner with business, engineering, and product teams to define analytical goals and success metrics. Translate complex data and model results into actionable insights for decision-makers. Advocate for data-driven culture and support data literacy across teams. 7. Continuous Improvement & Innovation Stay current with emerging trends in AI, ML, data visualization, and cloud technologies. Identify opportunities for process optimization, automation, and innovation. Contribute to internal R&D and AI product development initiatives. Required Skills & Qualifications Technical Skills Programming: Proficient in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, H2O.ai). Databases & Querying: Advanced SQL skills; experience with BigQuery, Redshift, or Azure Synapse is a plus. Cloud Expertise: Hands-on experience with one or more major platforms Google Cloud, AWS, or Azure. Visualization & Reporting: Skilled in Tableau, Power BI, Looker, or Excel (pivot tables, data modeling). Data Engineering: Familiarity with ETL tools (Airflow, dbt, or similar). Operating Systems: Strong proficiency with Linux/Unix for scripting and automation. Soft Skills Strong analytical, problem-solving, and critical-thinking abilities. Excellent communication and presentation skills, including data storytelling. Curiosity and creativity in exploring and interpreting data. Collaborative mindset, capable of working in cross-functional and fast-paced environments. Education & Certifications Bachelors degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. • Masters degree in Data Analytics, Machine Learning, or Business Intelligence preferred. • Relevant certifications are highly valued: • Google Cloud Professional Data Engineer • AWS Certified Data Analytics – Specialty • Microsoft Certified: Azure Data Scientist Associate TensorFlow Developer Certifications Why Join Hudson Data At Hudson Data, youll be part of a dynamic, innovative, and globally connected team that uses cutting-edge tools from AI and ML frameworks to cloud-based analytics platforms to solve meaningful problems. You’ll have the opportunity to grow, experiment, and make a tangible impact in a culture that values creativity, precision, and collaboration.